scholarly journals Research on a Face Real-time Tracking Algorithm Based on Particle Filter Multi-Feature Fusion

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1245 ◽  
Author(s):  
Tao Wang ◽  
Wen Wang ◽  
Hui Liu ◽  
Tianping Li
Keyword(s):  
Big Data ◽  
Particle Filter ◽  
Skin Color ◽  
Feature Fusion ◽  
Average Distance ◽  
Histogram Method ◽  
Face Features ◽  
The Face ◽  
Edge Features ◽  
Tracking Object

With the revolutionary development of cloud computing and internet of things, the integration and utilization of “big data” resources is a hot topic of the artificial intelligence research. Face recognition technology information has the advantages of being non-replicable, non-stealing, simple and intuitive. Video face tracking in the context of big data has become an important research hotspot in the field of information security. In this paper, a multi-feature fusion adaptive adjustment target tracking window and an adaptive update template particle filter tracking framework algorithm are proposed. Firstly, the skin color and edge features of the face are extracted in the video sequence. The weighted color histogram are extracted which describes the face features. Then we use the integral histogram method to simplify the histogram calculation of the particles. Finally, according to the change of the average distance, the tracking window is adjusted to accurately track the tracking object. At the same time, the algorithm can adaptively update the tracking template which improves the accuracy and accuracy of the tracking. The experimental results show that the proposed method improves the tracking effect and has strong robustness in complex backgrounds such as skin color, illumination changes and face occlusion.

PARTICLE FILTER BASED MULTI-CAMERA INTEGRATION FOR FACE 3D-POSE TRACKING

2006 ◽  
Vol 04 (04) ◽  
pp. 677-690 ◽  
Author(s):  
YUANTAO GU ◽  
YILUN CHEN ◽  
ZHENGWEI JIANG ◽  
KUN TANG
Keyword(s):  
Particle Filter ◽  
Skin Color ◽  
Proper Time ◽  
Probabilistic Method ◽  
Computational Cost ◽  
Human Head ◽  
Color Distribution ◽  
Pose Tracking ◽  
Face Region ◽  
The Face

Face tracking has many visual applications such as human-computer interfaces, video communications and surveillance. Color-based particle trackers have been proved robust and versatile for a modest computational cost. In this paper, a probabilistic method for integrating multi-camera information is introduced to track human face 3D-pose variations. The proposed method fuses information coming from several calibrated cameras via one color-based particle filter. The algorithm relies on the following novelties. First, the human head other than face is defined as the target of our algorithm. To distinguish the face region and hair region, a dual-color-ball is utilized to model the human head in 3D space. Second, to enhance the robustness to illumination variety, the Fisher criterion is applied to measure the separability of the face region and the hair region on the color histogram. Consequently, the color distribution template can be adapted at the proper time. Finally, the algorithm is performed based on the distributed framework, therefore the computation is implemented equally by all client processors. To demonstrate the performance of the proposed algorithm, several scenarios of visual tracking are tested in an office environment with three to four calibrated cameras. Experiments show that accurate tracking results are achieved, even in some difficult scenarios, such as the complete occlusion and the temptation of anything with skin color. Furthermore, the additional information of our track results, including the head posture and the face orientation schemes, can be used for further work such as face recognition and eye gaze estimation, which is also explained by elaborated designed experiments.

Robust tracking algorithm based on multi-feature fusion and particle filter

2010 ◽  
Vol 30 (3) ◽  
pp. 643-645 ◽  
Author(s):  
Wei ZENG ◽  
Gui-bin ZHU ◽  
Jie CHEN ◽  
Ding-ding TANG
Keyword(s):  
Particle Filter ◽  
Feature Fusion ◽  
Tracking Algorithm ◽  
Robust Tracking

What Makes a City Bikeable? A Study of Intercity and Intracity Patterns of Bicycle Ridership using Mobike Big Data Records

2020 ◽  
Vol 46 (1) ◽  
pp. 55-75
Author(s):  
Ying Long ◽  
Jianting Zhao
Keyword(s):  
Big Data ◽  
Average Distance ◽  
Policy Implications ◽  
Rating System ◽  
City Centre ◽  
Average Score ◽  
Policy Makers ◽  
Usage Frequency ◽  
Share Data ◽  
The City

This paper examines how mass ridership data can help describe cities from the bikers' perspective. We explore the possibility of using the data to reveal general bikeability patterns in 202 major Chinese cities. This process is conducted by constructing a bikeability rating system, the Mobike Riding Index (MRI), to measure bikeability in terms of usage frequency and the built environment. We first investigated mass ridership data and relevant supporting data; we then established the MRI framework and calculated MRI scores accordingly. This study finds that people tend to ride shared bikes at speeds close to 10 km/h for an average distance of 2 km roughly three times a day. The MRI results show that at the street level, the weekday and weekend MRI distributions are analogous, with an average score of 49.8 (range 0–100). At the township level, high-scoring townships are those close to the city centre; at the city level, the MRI is unevenly distributed, with high-MRI cities along the southern coastline or in the middle inland area. These patterns have policy implications for urban planners and policy-makers. This is the first and largest-scale study to incorporate mobile bike-share data into bikeability measurements, thus laying the groundwork for further research.

Face detection in single and multiple images using different skin color models

2020 ◽  
Vol 13 ◽  
Author(s):  
Manpreet Kaur ◽  
Jasdev Bhatti ◽  
Mohit Kumar Kakkar ◽  
Arun Upmanyu
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Input Image ◽  
Morphological Operations ◽  
Reliability Model ◽  
Multiple Images ◽  
Face Region ◽  
Bounding Box ◽  
The Face ◽  
Precision And Accuracy

Introduction: Face Detection is used in many different steams like video conferencing, human-computer interface, in face detection, and in the database management of image. Therefore, the aim of our paper is to apply Red Green Blue ( Methods: The morphological operations are performed in the face region to a number of pixels as the proposed parameter to check either an input image contains face region or not. Canny edge detection is also used to show the boundaries of a candidate face region, in the end, the face can be shown detected by using bounding box around the face. Results: The reliability model has also been proposed for detecting the faces in single and multiple images. The results of the experiments reflect that the algorithm been proposed performs very well in each model for detecting the faces in single and multiple images and the reliability model provides the best fit by analyzing the precision and accuracy. Moreover Discussion: The calculated results show that HSV model works best for single faced images whereas YCbCr and TSL models work best for multiple faced images. Also, the evaluated results by this paper provides the better testing strategies that helps to develop new techniques which leads to an increase in research effectiveness. Conclusion: The calculated value of all parameters is helpful for proving that the proposed algorithm has been performed very well in each model for detecting the face by using a bounding box around the face in single as well as multiple images. The precision and accuracy of all three models are analyzed through the reliability model. The comparison calculated in this paper reflects that HSV model works best for single faced images whereas YCbCr and TSL models work best for multiple faced images.

Face Image Segmentation Using Color Information and Saliency Map

2011 ◽  
Vol 55-57 ◽  
pp. 77-81
Author(s):  
Hui Ming Huang ◽  
He Sheng Liu ◽  
Guo Ping Liu
Keyword(s):  
Image Segmentation ◽  
Human Skin ◽  
Skin Color ◽  
Saliency Map ◽  
Face Image ◽  
Color Information ◽  
Attention Model ◽  
Face Area ◽  
Face Region ◽  
The Face

In this paper, we proposed an efficient method to address the problem of color face image segmentation that is based on color information and saliency map. This method consists of three stages. At first, skin colored regions is detected using a Bayesian model of the human skin color. Then, we get a chroma chart that shows likelihoods of skin colors. This chroma chart is further segmented into skin region that satisfy the homogeneity property of the human skin. The third stage, visual attention model are employed to localize the face region according to the saliency map while the bottom-up approach utilizes both the intensity and color features maps from the test image. Experimental evaluation on test shows that the proposed method is capable of segmenting the face area quite effectively，at the same time, our methods shows good performance for subjects in both simple and complex backgrounds, as well as varying illumination conditions and skin color variances.

scholarly journals Leviathan, or the Rudder of Public Health

2017 ◽  
Vol 2 (Suppl. 1) ◽  
pp. 1-8
Author(s):  
Denis Horgan ◽  
Walter Ricciardi
Keyword(s):  
Public Health ◽  
Big Data ◽  
New Technologies ◽  
Personalised Medicine ◽  
Member States ◽  
Reform Agenda ◽  
The World ◽  
The Face ◽  
The Eu ◽  
Do So

In the world of modern health, despite the fact that we've been blessed with amazing advances of late - the advent of personalised medicine is just one example - “change” for most citizens seems slow. There are clear discrepancies in availability of the best care for all, the divisions in access from country to country, wealthy to poor, are large. There are even discrepancies between regions of the larger countries, where access often varies alarmingly. Too many Member States (with their competence for healthcare) appear to be clinging stubbornly to the concept of “one-size-fits-all” in healthcare and often stifle advances possible through personalised medicine. Meanwhile, the legislative arena encompassing health has grown big and unwieldy in many respects. And bigger is not always better. The health advances spoken of above, an increased knowledge on the part of patients, the emergence of Big Data and more, are quickly changing the face of healthcare in Europe. But healthcare thinking across the EU isn't changing fast enough. The new technologies will certainly speak for themselves, but only if allowed to do so. Acknowledging that, this article highlights a positive reform agenda, while explaining that new avenues need to be explored.

scholarly journals Bidirectional feedback dynamic particle filter with big data for the particle degeneracy problem

2018 ◽  
Vol 23 (4) ◽  
pp. 463-478
Author(s):  
Xuefeng Yan ◽  
Xiangwen Feng ◽  
Chengbo Song ◽  
Xiaolin Hu
Keyword(s):  
Big Data ◽  
Particle Filter ◽  
Degeneracy Problem

scholarly journals REVIEW:APPLICATION OF BIG DATA AND IOT PROTECTED MODE IN AGRICULTURE

2021 ◽  
Vol 5 (12) ◽  
Author(s):  
Dr.Anita K.Patil ◽  
Dr.A.R. Laware
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Climatic Factors ◽  
Cost Effective ◽  
Big Data Analysis ◽  
Modern Agriculture ◽  
Agriculture Industry ◽  
The Face ◽  
Smart Agriculture ◽  
Protected Mode

Advance researches in the field of Internet of Things (IoT) are helping to make water management smarter and also used for optimizing consumption in the smart agriculture industry. Now days the development and research in Intelligent Smart Farming IoT based devices is turning the face of agriculture production in enhancing as well making it cost-effective and reducing wastage. To create environmental conditions suitable for the growth of animals and plants, modern agriculture that uses artificial techniques to change climatic factors such as temperature a highly efficient protected agriculture mode is used. To handle the increasing challenges of agricultural production, the complex agricultural ecosystems need to be better understood. Modern digital technology used for continuously monitoring the physical environment and producing large quantities of data in an unprecedented pace. For improving productivity the analysis of big data would enable farmers and companies to extract value from it. Moreover big data analysis is leading to advances in various industries; it has not yet been widely applied in agriculture. The objective of this paper is to perform a review on current studies and research works in agriculture which employs the recent practice of big data analysis, in order to solve various relevant problems.

scholarly journals Dissociation of early and late face-related processes in Autism Spectrum Disorder and Williams syndrome

2021 ◽  
Author(s):  
Alice Gomez ◽  
Guillaume Lio ◽  
Manuela Costa ◽  
Angela Sirigu ◽  
Caroline Demily
Keyword(s):  
Williams Syndrome ◽  
Autism Spectrum ◽  
Neural Signals ◽  
Face To Face ◽  
Face Features ◽  
Early Signal ◽  
Reverse Pattern ◽  
The Face ◽  
Evoked Activity ◽  
Multivariate Pattern

Abstract Williams syndrome (WS) and Autism Spectrum Disorders (ASD) are psychiatric conditions associated with atypical but opposite face-to-face interactions patterns: WS patients overly stare at others, ASD individuals escape eye contact. Whether these behaviors result from dissociable visual processes within the occipito-temporal pathways is unknown. Using high-density electroencephalography, multivariate pattern classification and group blind source separation, we searched for face-related neural signals that could best discriminate WS (N = 14), ASD (N = 14) and neurotypical populations (N = 14). We found two peaks in neurotypical participants: the first at 170ms, an early signal known to be implicated in low-level face features, the second at 260ms, a late component implicated in decoding salient face social cues. The late 260ms signal varied as a function of the distance of the eyes in the face stimulus with respect to the viewers’ fovea, meaning that it was strongest when the eyes were projected on the fovea and weakest when projected in the retinal periphery. Remarkably, both components were found distinctly impaired and preserved in WS and ASD. In WS, we could weakly decode the 170ms signal probably due to their relatively poor ability to process faces’ morphology while the late 260ms component shown to be eye sensitive was highly significant. The reverse pattern was observed in ASD participants who showed neurotypical like early 170ms evoked activity but impaired late evoked 260ms signal. Our study reveals a dissociation between WS and ASD patients and point at different neural origins for their social impairments.

scholarly journals Human Face Detection in Color Images

2021 ◽  
Author(s):  
Jun Gao
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Video Retrieval ◽  
Color Images ◽  
Human Face ◽  
Detection Scheme ◽  
Retrieval Systems ◽  
Human Face Detection ◽  
The Face ◽  
Video Retrieval Systems

Detection of human face has many realistic and important applications such as human and computer interface, face recognition, face image database management, security access control systems and content-based indexing video retrieval systems. In this report a face detection scheme will be presented. The scheme is designed to operate on color images. In the first stage of algorithm, the skin color regions are detected based on the chrominance information. A color segmentation stage is then employed to make skin color regions to be divided into smaller regions which have homogenous color. Then, we use the iterative luminance segmentation to further separate the detected skin region from other skin-colored objects such as hair, clothes, and wood, based on the high variance of the luminance component in the neighborhood of edges of objects. Post-processing is applied to determine whether skin color regions fit the face constrains on density of skin, size, shape and symmetry and contain the facial features such as eyes and mouths. Experimental results show that the algorithm is robust and is capable of detecting multiple faces in the presence of a complex background which contains the color similar to the skin tone.

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